Wide-area measurement based concurrent high energy consumption data instant perception transmission method and system

Abstract

The application discloses a high-energy-consumption data instant perception transmission method and system based on wide-area measurement, which comprises the following steps: collecting power consumption data of each high-energy-consumption device in a wide area, determining an optimal sub-packet sequence and an optimal decision in transmission according to a dynamic programming algorithm, selecting transmission of a sub-packet or spectrum sensing for each transmission according to the decision, solving a signal gain corresponding to an optimal spectrum efficiency of each channel by using an optimization gradient algorithm, obtaining a delay corresponding to the signal gain, fusing the delay and the spectrum efficiency to obtain a channel evaluation index, and selecting an optimal channel according to the channel evaluation index; and transmitting data through the optimal channel according to the optimal decision, comparing priorities of additional data transmission tasks and the sub-packet to be transmitted next, and selecting data to be transmitted next according to the priorities. The application improves channel utilization and reduces data transmission delay.

Description

Technical Field

[0001] This invention belongs to the field of data transmission technology, and more specifically, relates to a high-energy-consuming real-time sensing and transmission method and system based on wide-area measurement concurrency. Background Technology

[0002] High-energy-consuming equipment is widely present in industrial production, large-scale commercial operations, and many critical sectors. Accurate, comprehensive, and real-time collection and transmission of energy consumption data from such equipment is crucial for optimizing energy management, improving energy efficiency, and achieving energy conservation and emission reduction goals. Data acquisition networks for high-energy-consuming equipment typically employ a tree topology, requiring data to be transmitted layer by layer upwards. Under high load, limited bandwidth leads to slow data transmission, insufficient throughput, and consequently, data acquisition delays. This is particularly problematic for wide-area measurement data, which is often large in volume, making channel congestion and transmission delays highly likely. Furthermore, additional tasks may interrupt transmission, leading to bandwidth contention and other issues due to multi-task concurrency. Summary of the Invention

[0003] To address the shortcomings of existing technologies, this invention provides a high-energy-consuming real-time sensing and transmission method and system based on wide-area measurement concurrency.

[0004] The present invention adopts the following technical solution.

[0005] The first aspect of this invention proposes a high-energy-consuming real-time sensing and transmission method based on wide-area measurement concurrency, comprising the following:

[0006] Collect power consumption data of various high-energy-consuming devices in a wide area, align the data with timestamps, and denoise the data to form a data packet to be transmitted;

[0007] The optimal sub-packet sequence and the optimal decision during transmission are determined using a dynamic programming algorithm; the decision is whether to transmit sub-packets or perform spectrum sensing in each transmission.

[0008] The optimal gradient algorithm is used to solve for the signal gain corresponding to the optimal spectral efficiency of each channel, and the delay corresponding to the signal gain is obtained. The delay and spectral efficiency are fused to obtain the channel evaluation index, and the optimal channel is selected based on the channel evaluation index.

[0009] Data is transmitted through the optimal channel according to the optimal decision. If there is an additional data transmission task during transmission, its priority is compared with that of the next sub-packet to be transmitted. If the additional data transmission task has a higher priority, it will be transmitted first when the next sub-packet is to be transmitted, and then data transmission will continue according to the optimal decision. Otherwise, the next sub-packet will be transmitted normally. After the transmission is completed, the importance of the data transmission task is compared with that of the next sub-packet to be transmitted.

[0010] Preferably, the process of collecting power consumption data from various high-energy-consuming devices in a wide area, aligning the data with timestamps, and denoising the data to form a data packet to be transmitted specifically involves:

[0011] The types of high-energy-consuming equipment include large electric motors, transformers, boilers, heat exchange pressure vessels, elevators, and lifting machinery;

[0012] Denoising is performed using a denoising model constructed from a pre-trained two-layer BP neural network.

[0013] Preferably, the step of determining the optimal sub-packet sequence and the optimal decision during transmission based on the dynamic programming algorithm specifically involves:

[0014] The data packet is randomly split into different sub-packets, generating a sub-packet sequence. Sub-packets are transmitted sequentially according to the sequence, and a decision sequence for transmitting all sub-packets is generated, resulting in a sequence of transmission states for all decisions. ;in, For the first k The amount of data contained in all untransmitted sub-packets under this decision; For the first k The channel state under the next decision includes excellent, good, poor, and unable to transmit. The set of all channel states is denoted as S. For the first k The remaining spectrum sensing count under each decision is the maximum remaining spectrum sensing count minus the current spectrum sensing count; the total number of decisions is denoted as... M The objective function is calculated and repeated from the point of randomly splitting the data packet into different sub-packets until the objective function is minimized or the set number of iterations is reached.

[0015] Preferably, the constructed objective function is specifically:

[0016] The objective function is:

[0017]

[0018] in, For the first k The value function of each decision, Using the following formula and the value function of the last decision. The value is solved recursively in reverse:

[0019]

[0020] in, For the first k The amount of data contained in the sub-packet transmitted during the next decision transmission. The channel state is Real-time transmission The time of each data point; For channel state from Become State transition function; , , The first k+ The data volume, channel state, and remaining spectrum sensing count of all untransmitted sub-packets in the first decision; when the first... k+ When the first decision is to transmit a sub-packet equal - , equal When the first k+ When the first decision is spectrum sensing equal , equal -1; The time consumed for spectrum sensing.

[0021] Preferably, the step of using an optimized gradient algorithm to solve for the signal gain corresponding to the optimal spectral efficiency of each channel and obtaining the delay corresponding to the signal gain specifically involves:

[0022] With spectral efficiency The objective function of the gradient optimization algorithm is to maximize the integral within a given bandwidth, and the spectral efficiency is also considered. The signal gain multiplied by the current channel's signal spectral distribution Divide by the frequency response of the interfering channel The signal spectrum distribution is a Gaussian kernel function. The result of dividing the signal spectrum distribution by the frequency response of the interference channel is mapped to the time domain. The mapping result is the step size of the signal gain iteration. The signal gain of each channel is adjusted to the signal gain corresponding to the optimal spectral efficiency of the corresponding channel.

[0023] After obtaining the delay time of each channel's transmitted data after adjustment, subtract the maximum delay time threshold, and then normalize it to obtain the delay corresponding to the signal gain of each channel.

[0024] Preferably, the fusion of delay and spectral efficiency yields a channel evaluation index, and the optimal channel is selected based on the channel evaluation index, specifically as follows:

[0025]

[0026] in, For channel evaluation metrics, For spectral efficiency; For delay; The set delay sensitivity coefficient;

[0027] The channel with the highest channel evaluation index is selected as the optimal channel.

[0028] Preferably, if there is an additional data transmission task, the priority of comparing it with the next sub-packet to be transmitted is specifically: assigning importance scores to the additional data transmission task and the data packets to be transmitted after denoising the power consumption data of high-energy-consuming equipment.

[0029] The priority of additional data transmission tasks is: a weighted sum of the importance score of the additional data transmission task and the reciprocal of the time it takes for the amount of data contained in the additional data transmission task to be transmitted under the current channel state;

[0030] The priority of the next sub-packet to be transmitted is calculated as follows: the importance score of the data packet to be transmitted after noise reduction of the power consumption data of high-energy-consuming devices, the reciprocal of the time it takes for the data contained in the next sub-packet to be transmitted to be transmitted through the current channel state, and the reciprocal of the time required to transmit the remaining sub-packets.

[0031] Preferably, the time expected to be consumed in transmitting the remaining sub-packets is equal to the time expected to be consumed in transmitting all sub-packets minus the time consumed in the current transmission, and the time expected to be consumed in transmitting all sub-packets is the objective function corresponding to the optimal decision.

[0032] A second aspect of the present invention proposes a high-energy-consuming real-time data sensing and transmission system based on wide-area measurement concurrency, specifically:

[0033] Acquisition module: Used to collect power consumption data of various high-energy-consuming devices in a wide area, align the data with timestamps, and denoise the data to form a data packet to be transmitted;

[0034] Dynamic programming module: used to determine the optimal sub-packet sequence and the optimal decision during transmission based on the dynamic programming algorithm; the decision is whether to transmit sub-packets or perform spectrum sensing in each transmission;

[0035] Optimal channel selection module: The optimal channel selection module uses the optimization gradient algorithm to solve for the signal gain corresponding to the optimal spectral efficiency of each channel, and obtains the delay corresponding to the signal gain. The delay and spectral efficiency are fused to obtain the channel evaluation index, and the optimal channel is selected based on the channel evaluation index.

[0036] Transmission module: Used to transmit data through the optimal channel according to the optimal decision. If there is an additional data transmission task during transmission, its priority is compared with that of the next sub-packet to be transmitted. If the additional data transmission task has a higher priority, it will be transmitted first when the next sub-packet is to be transmitted, and then data transmission will continue according to the optimal decision. Otherwise, the next sub-packet will be transmitted normally. After the transmission is completed, it returns to compare its importance with that of the next sub-packet to be transmitted.

[0037] The beneficial effects of this invention are as follows: Compared with existing technologies, this invention ensures the temporal consistency and data quality of original electricity consumption data through timestamp alignment technology and advanced data denoising algorithms. This invention effectively solves the latency problem caused by traditional fixed packet segmentation methods by determining the optimal sub-packet segmentation scheme and transmission decisions through dynamic programming algorithms; and by dynamically selecting the optimal transmission channel based on gradient optimization, it significantly improves the channel's resource utilization. This invention, by calculating priorities, can respond in real time to sudden high-priority transmission tasks during operation, ensuring that critical data is always transmitted with priority, thus guaranteeing both the system's rapid response capability to emergencies and maintaining the orderliness of current data transmission. Attached Figure Description

[0038] Figure 1 This is a flowchart of this embodiment;

[0039] Figure 2 A comparison graph of transmission delays for different methods;

[0040] Figure 3 A graph comparing network throughput using different methods. Detailed Implementation

[0041] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of this invention. The embodiments described in this application are merely some embodiments of this invention, and not all embodiments. Based on the spirit of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the protection scope of this invention.

[0042] like Figure 1 As shown, Embodiment 1 of the present invention provides a high-energy-consuming real-time sensing and transmission method based on wide-area measurement concurrency, including the following:

[0043] Collect power consumption data of various high-energy-consuming devices in a wide area, align the data with timestamps, and denoise the data to form a data packet to be transmitted;

[0044] The optimal sub-packet sequence and the optimal decision during transmission are determined using a dynamic programming algorithm; the decision is whether to transmit sub-packets or perform spectrum sensing in each transmission.

[0045] The optimal gradient algorithm is used to solve for the signal gain corresponding to the optimal spectral efficiency of each channel, and the delay corresponding to the signal gain is obtained. The delay and spectral efficiency are fused to obtain the channel evaluation index, and the optimal channel is selected based on the channel evaluation index.

[0046] Data is transmitted through the optimal channel according to the optimal decision. If there is an additional data transmission task during transmission, its priority is compared with that of the next sub-packet to be transmitted. If the additional data transmission task has a higher priority, it will be transmitted first when the next sub-packet is to be transmitted, and then data transmission will continue according to the optimal decision. Otherwise, the next sub-packet will be transmitted normally. After the transmission is completed, the importance of the data transmission task is compared with that of the next sub-packet to be transmitted.

[0047] In this preferred embodiment, the step of collecting power consumption data from various high-energy-consuming devices in a wide area, aligning the data with timestamps, and then denoising it to form a data packet to be transmitted specifically involves:

[0048] The types of high-energy-consuming equipment include large electric motors, transformers, boilers, heat exchange pressure vessels, elevators, and lifting machinery;

[0049] Denoising is performed using a denoising model constructed from a pre-trained two-layer BP neural network.

[0050] The loss function of the denoising model is cross-entropy, and the formula is:

[0051]

[0052] in, The loss function; For the first The true value of each sample without noise; For the first The output value of a sample after denoising by a two-layer BP neural network; The total number of samples.

[0053] In this preferred embodiment, the step of determining the optimal sub-packet sequence and the optimal decision during transmission based on the dynamic programming algorithm specifically includes:

[0054] The data packet is randomly split into different sub-packets, generating a sub-packet sequence. Sub-packets are transmitted sequentially according to the sequence, and a decision sequence for transmitting all sub-packets is generated, resulting in a sequence of transmission states for all decisions. ;in, For the first k The amount of data contained in all untransmitted sub-packets under this decision; For the first k Channel state under the next decision This is the set of all channel states, including excellent, good, poor, and unable to transmit. For the first k The remaining number of spectrum sensing attempts under the next decision is the maximum number of remaining spectrum sensing attempts minus the number of current spectrum sensing attempts. M The total number of decisions; calculate the constructed objective function, starting by randomly splitting the data packet into different sub-packets and repeating the above steps until the objective function is minimized or the set number of iterations is reached.

[0055] In this preferred embodiment, the constructed objective function is specifically:

[0056] The objective function is:

[0057]

[0058] in, For the first k The value function of each decision, Through the following formula and The value is solved recursively in reverse:

[0059]

[0060] in, For the first k The amount of data contained in the sub-packet transmitted during the next decision transmission. The channel state is Real-time transmission The time of each data point, the channel state is... The channel capacity at that time is ,but ; For channel state from Become State transition function; , , The first k+ The data volume, channel state, and remaining spectrum sensing count of all untransmitted sub-packets in the first decision; when the first... k+ When the first decision is to transmit a sub-packet equal - , equal When the first k+ When the first decision is spectrum sensing equal , equal -1; The time consumed for spectrum sensing.

[0061] In this preferred embodiment, the... The value is as follows:

[0062] If the final decision involves the number of spectrum sensing operations Less than 0, or channel state Number of sub-packets that could not be transmitted and were not transmitted If the value is greater than 0, the transmission is considered to have failed. The value is equal to the set transmission failure value, which is greater than the set threshold; otherwise, It is 0.

[0063] In this preferred embodiment, the step of using an optimized gradient algorithm to solve for the signal gain corresponding to the optimal spectral efficiency of each channel and obtaining the delay corresponding to the signal gain specifically involves:

[0064] With spectral efficiency The objective function of the gradient optimization algorithm is to maximize the integral within a given bandwidth, and the spectral efficiency is... Signal gain multiplied by the current channel's signal spectrum distribution Divide by the frequency response of the interfering channel The signal spectrum distribution is a Gaussian kernel function. The result of dividing the signal spectrum distribution by the frequency response of the interference channel is mapped to the time domain. The mapping result is the step size of the signal gain iteration. The signal gain of each channel is adjusted to the signal gain corresponding to the optimal spectral efficiency of the corresponding channel.

[0065] After obtaining the delay time of each channel's transmitted data after adjustment, subtract the maximum delay time threshold, and then normalize it to obtain the delay corresponding to the signal gain of each channel.

[0066] In this preferred embodiment, the channel evaluation index is obtained by fusing delay and spectral efficiency, and the optimal channel is selected based on the channel evaluation index, specifically as follows:

[0067]

[0068] in, For channel evaluation metrics, For spectral efficiency; For delay; The set delay sensitivity coefficient;

[0069] The channel with the highest channel evaluation index is selected as the optimal channel.

[0070] In this preferred embodiment, if there is an additional data transmission task, the priority of comparing it with the next sub-packet to be transmitted is specifically: assigning importance scores to the additional data transmission task and the data packets to be transmitted after denoising the power consumption data of high-energy-consuming equipment.

[0071] The priority of additional data transmission tasks is: a weighted sum of the importance score of the additional data transmission task and the reciprocal of the time it takes for the amount of data contained in the additional data transmission task to be transmitted under the current channel state;

[0072] The priority of the next sub-packet to be transmitted is calculated as follows: the importance score of the data packet to be transmitted after noise reduction of the power consumption data of high-energy-consuming devices, the reciprocal of the time it takes for the data contained in the next sub-packet to be transmitted to be transmitted through the current channel state, and the reciprocal of the time required to transmit the remaining sub-packets.

[0073] In this preferred embodiment, the time expected to be consumed in transmitting the remaining sub-packets is equal to the time expected to be consumed in transmitting all sub-packets minus the current transmission time, and the time expected to be consumed in transmitting all sub-packets is the objective function corresponding to the optimal decision.

[0074] Specifically, the priority formula is as follows:

[0075]

[0076] in, Prioritize the next sub-packet to be transmitted. Prioritize additional data transfer tasks; The importance score is generated by denoising the power consumption data of high-energy-consuming equipment to form the data packet to be transmitted; The importance score for the additional data transfer task is between 0 and 1; The expected time consumed to transmit all sub-packets; The time consumed by the current transmission; The amount of data to be transmitted in the next sub-packet will be transmitted within the current channel state. The amount of data included in additional data transmission tasks and the time it takes to transmit it through the current channel state; , The set coefficient.

[0077] After data acquisition, data is transmitted using the methods described in this embodiment: packet segmentation without dynamic programming, packet segmentation with fixed sizes, sensing with a fixed number of spectrum sensing operations, and random channel selection instead of choosing the optimal channel; transmission delay is as follows: Figure 2 As shown, it can be seen that the transmission delay time in this embodiment is the shortest; for the method proposed in this embodiment and the method that randomly selects a channel instead of selecting the optimal channel, different maximum iteration numbers are set in the dynamic programming algorithm, such as... Figure 3 As shown in the figure, the network throughput of the method proposed in this embodiment is also optimal.

[0078] Embodiment 2 of the present invention proposes a high-energy-consuming real-time data sensing and transmission system based on wide-area measurement concurrency, using the method described in Embodiment 1 of the present invention. Specifically, it is as follows:

[0079] Acquisition module: Used to collect power consumption data of various high-energy-consuming devices in a wide area, align the data with timestamps, and denoise the data to form a data packet to be transmitted;

[0080] Dynamic programming module: used to determine the optimal sub-packet sequence and the optimal decision during transmission based on the dynamic programming algorithm; the decision is whether to transmit sub-packets or perform spectrum sensing in each transmission;

[0081] Optimal channel selection module: The optimal channel selection module uses the optimization gradient algorithm to solve for the signal gain corresponding to the optimal spectral efficiency of each channel, and obtains the delay corresponding to the signal gain. The delay and spectral efficiency are fused to obtain the channel evaluation index, and the optimal channel is selected based on the channel evaluation index.

[0082] Transmission module: Used to transmit data through the optimal channel according to the optimal decision. If there is an additional data transmission task during transmission, its priority is compared with that of the next sub-packet to be transmitted. If the additional data transmission task has a higher priority, it will be transmitted first when the next sub-packet is to be transmitted, and then data transmission will continue according to the optimal decision. Otherwise, the next sub-packet will be transmitted normally. After the transmission is completed, it returns to compare its importance with that of the next sub-packet to be transmitted.

[0083] This disclosure can be a system, method, and / or computer program product. A computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for causing a processor to implement various aspects of this disclosure.

[0084] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the claims of the present invention.

Claims

1. A high-energy-consuming real-time sensing and transmission method based on wide-area measurement concurrency, characterized in that, Includes the following: Collect power consumption data of various high-energy-consuming devices in a wide area, align the data with timestamps, and denoise the data to form a data packet to be transmitted; The optimal sub-packet sequence and the optimal decision during transmission are determined using a dynamic programming algorithm; the decision is whether to transmit sub-packets or perform spectrum sensing in each transmission, specifically: The data packet is randomly split into different sub-packets, generating a sub-packet sequence. Sub-packets are transmitted sequentially according to the sequence, and a decision sequence for transmitting all sub-packets is generated, resulting in a sequence of transmission states for all decisions. ;in, For the first k The amount of data contained in all untransmitted sub-packets under this decision; For the first k The channel state under the next decision includes excellent, good, poor, and unable to transmit. The set of all channel states is denoted as S. For the first k The remaining spectrum sensing count under each decision is the maximum remaining spectrum sensing count minus the current spectrum sensing count; the total number of decisions is denoted as... M ;Calculate the constructed objective function, starting by randomly splitting the data packet into different sub-packets, repeating the above steps until the objective function is minimized or the set number of iterations is reached; The constructed objective function is specifically as follows: The objective function is: in, For the first k The value function of each decision, each Using the following formula and the value function of the last decision. The value is solved recursively in reverse: in, For the first k The amount of data contained in the sub-packet transmitted during the next decision transmission. The channel state is Real-time transmission The time of each data point; For channel state from Become State transition function; , , The first k+ The data volume, channel state, and remaining spectrum sensing count of all untransmitted sub-packets in the first decision; when the first decision... k+ When the first decision is to transmit a sub-packet equal - , equal When the first k+ When the first decision is spectrum sensing equal , equal -1; Time consumed for spectrum sensing; The optimal gradient algorithm is used to solve for the signal gain corresponding to the optimal spectral efficiency of each channel, and the delay corresponding to the signal gain is obtained. The delay and spectral efficiency are fused to obtain the channel evaluation index, and the optimal channel is selected based on the channel evaluation index. Data is transmitted through the optimal channel according to the optimal decision. If there is an additional data transmission task during transmission, its priority is compared with that of the next sub-packet to be transmitted. If the additional data transmission task has a higher priority, it will be transmitted first when the next sub-packet is to be transmitted, and then data transmission will continue according to the optimal decision. Otherwise, the next sub-packet will be transmitted normally. After the transmission is completed, the importance of the data transmission task is compared with that of the next sub-packet to be transmitted.

2. The high-energy-consuming real-time sensing and transmission method based on wide-area measurement concurrency according to claim 1, characterized in that: The process of collecting power consumption data from various high-energy-consuming devices in a wide area, aligning the data with timestamps, and denoising the data to form a data packet to be transmitted is as follows: The types of high-energy-consuming equipment include large electric motors, transformers, boilers, heat exchange pressure vessels, elevators, and lifting machinery; Denoising is performed using a denoising model constructed from a pre-trained two-layer BP neural network.

3. The high-energy-consuming real-time sensing and transmission method based on wide-area measurement concurrency according to claim 1, characterized in that: The The value is as follows: If the final decision involves the number of spectrum sensing operations Less than 0, or channel state Number of sub-packets that could not be transmitted and were not transmitted If the value is greater than 0, the transmission is considered to have failed. The value is equal to the set transmission failure value, which is greater than the set threshold; otherwise, It is 0.

4. The high-energy-consuming real-time sensing and transmission method based on wide-area measurement concurrency according to claim 1, characterized in that: The step of using an optimized gradient algorithm to solve for the signal gain corresponding to the optimal spectral efficiency of each channel, and obtaining the delay corresponding to the signal gain, specifically involves: With spectral efficiency The objective function of the gradient optimization algorithm is to maximize the integral within a given bandwidth, and the spectral efficiency is also considered. The signal gain multiplied by the current channel's signal spectral distribution Divide by the frequency response of the interfering channel The signal spectrum distribution is a Gaussian kernel function. The result of dividing the signal spectrum distribution by the frequency response of the interference channel is mapped to the time domain. The mapping result is the step size of the signal gain iteration. The signal gain of each channel is adjusted to the signal gain corresponding to the optimal spectral efficiency of the corresponding channel. After obtaining the delay time of each channel's transmitted data after adjustment, subtract the maximum delay time threshold, and then normalize it to obtain the delay corresponding to the signal gain of each channel.

5. The high-energy-consuming real-time sensing and transmission method based on wide-area measurement concurrency according to claim 4, characterized in that: The fusion of delay and spectral efficiency yields a channel evaluation index. Based on this index, the optimal channel is selected, specifically as follows: in, For channel evaluation metrics, For spectral efficiency; For delay; The set delay sensitivity coefficient; The channel with the highest channel evaluation index is selected as the optimal channel.

6. The high-energy-consuming real-time sensing and transmission method based on wide-area measurement concurrency according to claim 1, characterized in that: If there is an additional data transmission task, its priority is compared with that of the next sub-packet to be transmitted. Specifically, importance scores are assigned to the additional data transmission task and the data packets to be transmitted after the power consumption data of high-energy-consuming equipment is denoised. The priority of additional data transmission tasks is: a weighted sum of the importance score of the additional data transmission task and the reciprocal of the time it takes for the amount of data contained in the additional data transmission task to be transmitted under the current channel state; The priority of the next sub-packet to be transmitted is calculated as follows: the importance score of the data packet to be transmitted after noise reduction of the power consumption data of high-energy-consuming devices, the reciprocal of the time it takes for the data contained in the next sub-packet to be transmitted to be transmitted through the current channel state, and the reciprocal of the time required to transmit the remaining sub-packets.

7. The high-energy-consuming real-time sensing and transmission method based on wide-area measurement concurrency according to claim 6, characterized in that: The expected time to transmit the remaining sub-packets is equal to the expected time to transmit all sub-packets minus the current transmission time, and the expected time to transmit all sub-packets is the objective function corresponding to the optimal decision.

8. A high-energy-consuming real-time data sensing and transmission system based on wide-area measurement concurrency according to any one of claims 1-7, characterized in that: Acquisition module: Used to collect power consumption data of various high-energy-consuming devices in a wide area, align the data with timestamps, and denoise the data to form a data packet to be transmitted; Dynamic programming module: used to determine the optimal sub-packet sequence and the optimal decision during transmission based on the dynamic programming algorithm; the decision is whether to transmit sub-packets or perform spectrum sensing in each transmission; Optimal channel selection module: The optimal channel selection module uses the optimization gradient algorithm to solve for the signal gain corresponding to the optimal spectral efficiency of each channel, and obtains the delay corresponding to the signal gain. The delay and spectral efficiency are fused to obtain the channel evaluation index, and the optimal channel is selected based on the channel evaluation index. Transmission module: Used to transmit data through the optimal channel according to the optimal decision. If there is an additional data transmission task during transmission, its priority is compared with that of the next sub-packet to be transmitted. If the additional data transmission task has a higher priority, it will be transmitted first when the next sub-packet is to be transmitted, and then data transmission will continue according to the optimal decision. Otherwise, the next sub-packet will be transmitted normally. After the transmission is completed, it returns to compare its importance with that of the next sub-packet to be transmitted.

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